Analysts, traders, and even individual investors consistently look for ways to maximize their profits. These individuals have a keen interest in determining whether a tradable financial instrument will go up or down in price and whether they should change their respective ownership positions accordingly. Unfortunately, systems of the prior art rely heavily upon past price performance for the tradable instrument, which may include any item of value that can be subject to exchange in any market (e.g., stocks, bonds, mutual funds, commodities, commercial paper, currencies, options on these kinds of instruments, and the like). With the advancement of international markets and exchanges, the need for more precise systems for trading securities and other instruments has become a world-wide endeavor. A system with the ability to predict the performance of a traded instrument would have around the clock applicability.
Traders have utilized various systems to analyze the price performance of tradable instruments. Some of the simpler systems track the historical performance and use averages over time to predict performance. Other systems plot the cycles of price performance with the hope that the cycles repeat themselves over time. Expert analysts create wildly complicated formulas for incorporating numerous variables into the equation, such as current economic conditions in specific regions, technological advancements in particular market sectors, and even the weather reports for commodities exchanges. All of these systems have a degree of unpredictability because markets change too fast for historical performance and objective scientific facts to keep up with an emotion-driven marketplace that changes with instantaneous news traveling all over the world.
U.S. Pat. No. 7,848,995 (Dalal 2010) shows one prior system that has attempted to consolidate both historical and prospective approaches to price changes in a trading market. Dalal uses a graphical representation of the buying pressure and the selling pressure for a respective tradable instrument over numerous time periods to determine trends in price changes. Dalal uses real time plots of the buying and selling pressure in the marketplace as graphical lines for visual cues of demand. The buying and selling pressure lines represent historical collective desire to buy or sell an instrument. Dalal tracks trends by analyzing the open, close, high, and low market prices for a variety of time periods and determines where the buying pressure line crosses the selling pressure line, or vice versa, to establish a change in the buying or selling trend. Dalal looks for changes in the trends over several different time periods to establish an alignment that indicates a true trend reversal in buying and selling pressure. The pressure lines that Dalal plots are created by “algorithms using mathematical formulas based on open, high, low, close prices of the Market Vehicle for a particular time frame either independently, or in a combination with some variable factors and constants.” Col. 18, Lines 18-21. Dalal gives an example that the pressure lines may be based upon a tradable instrument's average open and close price for a variety of time frames. Dalal uses a natural log function as an exponential to normalize the data for plotting different time periods in the same graph. Data other than close prices may be used for charting (i.e., bid and ask prices noted in col. 18, lines 29-30). Dalal's theory of plotting buying and selling pressure relies extensively upon the concept that as selling pressure V1 begins peaking, the market will soon begin heading in a bullish (buying) direction, and when the buying pressure is on the rise, the market will soon be heading in a bearish direction because the buying trend will eventually weaken. Dalal marks the cross points of the trend waves as indicators of a new direction for that instrument. The trends are based upon collective historical data of open, close, high, and low points for the price of the trade. In other words, Dalal's data is embedded with historical data, and the trends are represented as waves of data over periods of time.
Dalal's FIGS. 5 and 6 show the trend analysis at a crossing point of buying pressure and selling pressure waves. Dalal notes that when the closing price of a tradable instrument begins moving downward and the selling pressure wave is moving upward toward a higher buying pressure wave, then the transition from bullish to bearish is approaching. When the closing price of a tradable instrument begins moving downward and the buying pressure begins moving downward toward a lower selling pressure, then again the trend is changing from bullish to bearish for that instrument. When the closing price begins moving upward in circumstances when either the buying pressure increases toward a higher selling pressure wave or the selling pressure wave decreases toward a lower buying pressure wave, then that instrument is moving from bearish to bullish.
Dalal's algorithm relies entirely upon trends over time to determine how to plot the buying and selling pressure waves and to normalize them with the natural logarithm exponential (another indication of how time periods are embedded within Dalal's algorithm). To enhance the original algorithm and its associated trend waves, Dalal further plots the highest closing price and the lowest closing price as individual “dots” superimposed over the wave paths representing buying and selling pressure. The positions of the highest and lowest closing prices are further indicators of trends. Generally, the position of the highest closing prices dots will be below the buying pressure wave plot when stronger selling pressure exists and will be above the buying pressure wave plot when stronger buying pressure exists in a market. The position of the lowest closing price dots will be above the selling pressure line when stronger selling pressure exists and will be below the buying pressure line stronger buying pressure exists in a market.
Dalal notes that the system described in the '995 patent that one technical indicator is available by calculating pivot points defined as a particular stock's high, low, and closing prices. If the following day's market price falls below the pivot point (i.e., below the average), that pivot point may indicate a trend reversal downward and a new resistance level. “Conversely, if the market price rises above the pivot point, it may act as the new support level.” See Dalal, Col. 12, Lines 6-12.
Dalal continued this line of research in a follow-up continuation in part application published as United States Patent Publication No. 20080313560 (Dec. 18, 2008). Notably, Dalal, incorporated the pivot point concept as a way of calculating traditional support and resistance lines that are known in the art today. Support lines indicate support for an upward price trend, and resistance lines indicate resistance to continued upward movement in price. Dalal uses the pivot points, defined as the average of the high, low, and close values for a traded instrument, to calculate resistance and support points for the respective line plots. Dalal's formulas for calculating the resistance and support points are detailed in Dalal's '560 publication at paragraphs [0358] and [0359]. Notably, the resistance and support lines utilize the previously calculated pivot points with particular weighting (i.e., the resistance point is waiting toward the highest price of the instrument, and the support points are weighted toward the lowest price paid for the instrument). Dalal notes in particular that pivot calculations shift with time, as Dalal uses a “new point in-last point out system” so that the data shifts with time. Dalal further describes the use of a moving average of pivot points to smooth out the data. This is in line with Dalal's overall interest in a collective history in each data point.
Even with Dalal's research and other similar attempts at predicting trading points in a market, a need exists for a system that more accurately provides up to date information regarding the price of a traded instrument.